Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior

This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from observed multiple low-resolution images. A compound Gaussian MRF model provides a preferable prior for natural images that preserves edges. PM is the optimal estimator for the objective function of peak signal-to-noise ratio (PSNR). This estimator is numerically determined by using variational Bayes (VB). We then solve the conjugate prior problem on VB and the exponential-order calculation cost problem of a compound Gaussian MRF prior with simple Taylor approximations. In experiments, the proposed method roughly overcomes existing methods.Comment: 5 pages, 20 figures, 1 tables, accepted to ICASSP2012 (corrected 2012/3/23

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Single Frame Image super Resolution using Learned Directionlets

In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training set for super resolving a low resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are learned locally from this training set and the inverse Directionlet transform recovers the super-resolved high resolution image. The simulation results showed that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values. This method gives good result with aliased images also.Comment: 14 pages,6 figure

The K2-ESPRINT Project III: A Close-in Super-Earth around a Metal-rich Mid-M Dwarf

We validate a $R_p=2.32\pm 0.24R_\oplus$ planet on a close-in orbit ($P=2.260455\pm 0.000041$ days) around K2-28 (EPIC 206318379), a metal-rich M4-type dwarf in the Campaign 3 field of the K2 mission. Our follow-up observations included multi-band transit observations from the optical to the near infrared, low-resolution spectroscopy, and high-resolution adaptive-optics (AO) imaging. We perform a global fit to all the observed transits using a Gaussian process-based method and show that the transit depths in all passbands adopted for the ground-based transit follow-ups ($r'_2, z_\mathrm{s,2}, J, H, K_\mathrm{s}$) are within $\sim 2\sigma$ of the K2 value. Based on a model of the background stellar population and the absence of nearby sources in our AO imaging, we estimate the probability that a background eclipsing binary could cause a false positive to be $< 2\times 10^{-5}$. We also show that K2-28 cannot have a physically associated companion of stellar type later than M4, based on the measurement of almost identical transit depths in multiple passbands. There is a low probability for a M4 dwarf companion ($\approx 0.072_{-0.04}^{+0.02}$), but even if this were the case, the size of K2-28b falls within the planetary regime. K2-28b has the same radius (within $1\sigma$) and experiences a similar irradiation from its host star as the well-studied GJ~1214b. Given the relative brightness of K2-28 in the near infrared ($m_\mathrm{Kep}=14.85$ mag and $m_H=11.03$ mag) and relatively deep transit ($0.6-0.7\%$), a comparison between the atmospheric properties of these two planets with future observations would be especially interesting.Comment: 11 pages, 9 figures, accepted to Ap

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods

The Perception-Distortion Tradeoff

Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true for any distortion measure, and is not only a problem of the PSNR or SSIM criteria. We also show that generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. This constitutes theoretical support to their observed success in low-level vision tasks. Based on our analysis, we propose a new methodology for evaluating image restoration methods, and use it to perform an extensive comparison between recent super-resolution algorithms.Comment: CVPR 2018 (long oral presentation), see talk at: https://youtu.be/_aXbGqdEkjk?t=39m43